Skip to main content

Peptide-Protein Interface Classification Using Convolutional Neural Networks

  • Conference paper
  • First Online:
Advances in Bioinformatics and Computational Biology (BSB 2023)


Peptides are short chains of amino acid residues linked through peptide bonds, whose potential to act as protein inhibitors has contributed to the advancement of rational drug design. Indeed, understanding the interactions between proteins and peptides is potentially helpful for several biotechnological applications. However, it is not a trivial task since peptides can adopt different conformations when interacting with proteins. In this paper, we develop a classification model for protein-peptide interfaces using a convolutional neural network and distance maps. To evaluate our proposal, we performed two case studies classifying protein-peptide interfaces based on peptide sequences and receptor classes. Additionally, we compared the distance map approach with a graph-based structural signatures approach. We aim to find out if a convolutional neural network could classify peptides just from the patterns of distances in these maps. In conclusion, graph-based methods were slightly superior in almost all comparisons performed. However, distance map-based signature methods achieved better results for some classes, such as classifying hormones, membranes, and viral proteins. These results shed light on the potential use of distance maps for classifying protein-peptide interfaces. Nevertheless, more experiments may be needed to explore this use.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. 1.


  1. Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems, pp. 1–16 (2016). arXiv:1603.04467

  2. Angelova, A., Drechsler, M., Garamus, V.M., Angelov, B.: Pep-lipid cubosomes and vesicles compartmentalized by micelles from self-assembly of multiple neuroprotective building blocks including a large peptide hormone PACAP-DHA. ChemNanoMat 5(11), 1381–1389 (2019).

    Article  CAS  Google Scholar 

  3. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013).

    Article  PubMed  Google Scholar 

  4. Chollet, F.: Deep Learning with Python, 4th edn. Manning, New York (2021)

    Google Scholar 

  5. Das, A.A., Sharma, O.P., Kumar, M.S., Krishna, R., Mathur, P.P.: PepBind: a comprehensive database and computational tool for analysis of protein-peptide interactions. Genom. Proteom. Bioinform. 11(4), 241–246 (2013).

    Article  Google Scholar 

  6. Defresne, M., Sophie, B., Thomas, S.: Protein design with deep learning. Int. J. Mol. Sci. 22, 1741 (2021)

    Article  Google Scholar 

  7. Demšar, J., et al.: Orange: data mining toolbox in Python. J. Mach. Learn. Res. 14(1), 2349–2353 (2013).

    Article  Google Scholar 

  8. Duda, R., Hart, P., Stork, G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    Google Scholar 

  9. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. Adaptive Computation And Machine Learning, MIT Press, Cambridge (2016)

    Google Scholar 

  10. Ioffe, S., Szegedy, C.: Batch Normalization: accelerating deep network training by reducing internal covariate shift. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on International Conference on Machine Learning, vol. 37, pp. 448–456 (2015). arXiv:1502.03167

  11. Iyer, M., Jaroszewski, L., Sedova, M., Godzik, A.: What the protein data bank tells us about the evolutionary conservation of protein conformational diversity. Protein Sci. 31, e4325 (2022).

  12. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015 (2015). arXiv:1412.6980

  13. Kloczkowski, A., et al.: Distance matrix-based approach to protein structure prediction. J. Struct. Funct. Genom. 10(1), 67–81 (2009).

    Article  CAS  Google Scholar 

  14. Lau, J.L., Dunn, M.K.: Therapeutic peptides: historical perspectives, current development trends, and future directions. Bioorg. Med. Chem. 26(10), 2700–2707 (2018).

    Article  CAS  PubMed  Google Scholar 

  15. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989).

    Article  Google Scholar 

  16. London, N., Movshovitz-Attias, D., Schueler-Furman, O.: The structural basis of peptide-protein binding strategies. Structure 18(2), 188–199 (2010).

    Article  CAS  PubMed  Google Scholar 

  17. Mariano, D., et al.: A computational method to propose mutations in enzymes based on structural signature variation (SSV). Int. J. Mol. Sci. 20(2), 333 (2019).

  18. Martins, P.M., Santos, L.H., Mariano, D., et al.: Propedia: a database for protein-peptide identification based on a hybrid clustering algorithm. BMC Bioinform. 22, 1 (2021).

    Article  CAS  Google Scholar 

  19. Martins, P., et al.: Propedia v2.3: a novel representation approach for the peptide-protein interaction database using graph-based structural signatures. Front. Bioinform. 3, 1103103 (2023).

  20. Melo, R.C., et al.: Finding protein-protein interaction patterns by contact map matching. Genet. Mol. Res. 6(4), 946–963 (2007)

    CAS  PubMed  Google Scholar 

  21. Min, S., Lee, B., Yoon, S.: Deep learning in bioinformatics. Brief. Bioinform. 18(5), 851–869 (2017).

    Article  PubMed  Google Scholar 

  22. Mishkin, D., Sergievskiy, N., Matas, J.: Systematic evaluation of convolution neural network advances on the ImageNet. Comput. Vis. Image Underst. 161, 11–19 (2017).

    Article  Google Scholar 

  23. Moreno-Camacho, C.A., Montoya-Torres, J.R., Jaegler, A., Gondran, N.: Sustainability metrics for real case applications of the supply chain network design problem: a systematic literature review. J. Clean. Prod. 231, 600–618.

  24. Mosteller, F., Tukey, J.: Data analysis, including statistics. In: Lindzey, G., Aronson, E. (eds.) Revised Handbook of Social Psychology, vol. 2, pp. 80–203 (1968)

    Google Scholar 

  25. Pires, D.E.V., de Melo-Minardi, R.C., da Silveira, C.H., Campos, F.F., Meira, W.: aCSM: noise-free graph-based signatures to large-scale receptor-based ligand prediction. Bioinformatics 29(7), 855–861 (2013).

    Article  CAS  PubMed  Google Scholar 

  26. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929–1958 (2014).

    Article  Google Scholar 

  27. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 2nd edn. Academic Press, Burlington (2009)

    Google Scholar 

  28. Vinogradov, A.A., Yin, Y., Suga, H.: Macrocyclic peptides as drug candidates: recent progress and remaining challenges. J. Am. Chem. Soc. 141(10), 4167–4181 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Webb, A., Copsey, K.: Statistical Pattern Recognition. Wiley, New York (2011)

    Book  Google Scholar 

  30. Xu, M., Yoon, S., Fuentes, A., Park, D.S.: A Comprehensive survey of image augmentation techniques for deep learning. Pattern Recogn. 137, 109347 (2023).

Download references


The authors thank the funding agencies: CAPES, CNPq, and FAPEMIG.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Lucas Moraes dos Santos .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Santos, L.M.d., Mariano, D., Luiza Bastos, L., Cioletti, A.G., Minardi, R.C.d.M. (2023). Peptide-Protein Interface Classification Using Convolutional Neural Networks. In: Reis, M.S., de Melo-Minardi, R.C. (eds) Advances in Bioinformatics and Computational Biology. BSB 2023. Lecture Notes in Computer Science(), vol 13954. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42714-5

  • Online ISBN: 978-3-031-42715-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics